This vignette will guide you through the livestock-related data available within the Evidence for Resilient Agriculture (ERA) dataset. The dataset serves as a critical resource for understanding agricultural resilience, specifically focusing on livestock systems and their adaptation to climate change.

We will cover the following:

Methodology:

1. Search Terms Used

2. Screening Criteria Applied

3. Data Extraction Process

Exploration of Livestock Data in ERA:

1. Exploring geographic locations of studies available

2. Practices in changing diets

3. Understanding Diet compositions

4. Common outcomes reported

Methodology

ERA Search Terms

Our initial search, conducted in 2014, queried the Web of Science and Scopus Databases for English language articles. Database queries consisted of key terms describing three components: technology, outcome, and geographic location. Given the myriad outcomes of interest, we constructed distinct search strings for each category: productivity, resilience, and mitigation outcomes, as well as a key term related to barriers to adoption.

Our methodology employed Boolean operators ‘OR’ and ‘AND’ to ensure the comprehensiveness of the search. This string for each technology category was run in both search engines for each of the outcome categories, ‘productivity,’ ‘resilience,’ ‘mitigation,’ and additionally for ‘barriers’.

Below in a snippet of the search terms used for the livestock extraction:

Search Terms for ERA Livestock
Source Subject Query
WoS Livestock (Livestock OR ‘mono gastric’ OR cattle OR sheep OR goats OR pigs OR poultry OR ruminant OR aquaculture OR fish) AND (‘non-conventional feed’ OR ‘Forage productivity’ OR grass OR ‘pasture additive’ OR ‘grass-legume’ OR ‘feed conversion’ OR ‘feed intake’ OR ‘protein intake’ OR ‘energy intake’ OR ‘feed availability’ OR ‘feed supplement’ OR ’energy retention’ OR ’growth rate’ OR ’feed acceptability’ OR ’feeding frequency’ OR ’stover digestibility’ OR ’paddock’ OR ’freerange’ OR ‘hay’ OR ‘silage’ OR ‘fodder shrub’ OR ’nomadic’ OR pastoral OR ’signalgrass’ OR (pasture NEAR cerrado) OR ‘crop residue’ OR ‘animal husbandry’ OR ‘pasture species’ OR ‘crop-pasture’ OR ‘pasture crop’ OR ’zero graz’ OR ‘rotational graz’ OR ’conti graz’ OR ’stocking density’ OR ’organic livestock’ OR ‘ammonia volatil’ OR ’N-retention’ OR ’cover manure’ OR ‘biogas capture’ OR ‘Manure acidification’ OR ‘Cover* manure’ OR ‘Manure collection’ OR ‘manure treatment’ OR ‘artificial insemination’ OR ‘trait selection’ OR ‘heat period’ OR ovulation OR hybrid OR ‘desirable traits’ OR ‘progeny test’ OR ‘semen analysis’ OR ‘cross breed’ OR ’Aquasilviculture’ OR ’Integrated multi-trophic aquaculture’ OR ’Organic Aquaculture’ OR ’fishing intensity’ OR ’culture based fishery’ OR ’vulnerable’ OR ’susceptible’ OR ’resistan’ OR ‘quarantine’ OR ‘antibiotic’ OR ‘vaccine’ OR ‘dewormer’ OR ‘ectoparasite’ OR ‘innoculation’ OR ’agropasto’ OR (Livestock AND (antistress OR ‘anti-stress’)))
Scopus Livestock (Livestock OR ‘mono gastric’ OR cattle OR sheep OR goats OR pigs OR poultry OR ruminant OR aquaculture OR fish) AND (‘non-conventional feed’ OR ‘Forage productivity’ OR grass OR ‘pasture additive’ OR ‘grass-legume’ OR ‘feed conversion’ OR ‘feed intake’ OR ‘protein intake’ OR ‘energy intake’ OR ‘feed availability’ OR ‘feed supplement’ OR ’energy retention’ OR ’growth rate’ OR ’feed acceptability’ OR ’feeding frequency’ OR ’stover digestibility’ OR ’paddock’ OR ’freerange’ OR ‘hay’ OR ‘silage’ OR ‘fodder shrub’ OR ’nomadic’ OR pastoral OR ’signalgrass’ OR (pasture W/ cerrado) OR ‘crop residue’ OR ‘animal husbandry’ OR ‘pasture species’ OR ‘crop-pasture’ OR ‘pasture crop’ OR ’zero graz’ OR ‘rotational graz’ OR ’conti graz’ OR ’stocking density’ OR ’organic livestock’ OR ‘ammonia volatil’ OR ’N-retention’ OR ’cover manure’ OR ‘biogas capture’ OR ‘Manure acidification’ OR ‘Cover* manure’ OR ‘Manure collection’ OR ‘manure treatment’ OR ‘artificial insemination’ OR ‘trait selection’ OR ‘heat period’ OR ovulation OR hybrid OR ‘desirable traits’ OR ‘progeny test’ OR ‘semen analysis’ OR ‘cross breed’ OR ’Aquasilviculture’ OR ’Integrated multi-trophic aquaculture’ OR ’Organic Aquaculture’ OR ’fishing intensity’ OR ’culture based fishery’ OR ’vulnerable’ OR ’susceptible’ OR ’resistan’ OR ‘quarantine’ OR ‘antibiotic’ OR ‘vaccine’ OR ‘dewormer’ OR ‘ectoparasite’ OR ‘innoculation’ OR ’agropasto’ OR (Livestock AND (antistress OR ‘anti-stress’)))

Screening Criteria with PICOS Standards

This screening criteria is designed to systematically review papers focusing on livestock management and outcomes, applying both inclusion and exclusion criteria based on specific parameters. The framework ensures that the selected studies align with the objectives of the review, particularly within the context of livestock systems in Africa.

PICOS Framework The PICOS framework helps structure systematic reviews by defining key components: Population, Intervention, Comparison, Outcomes, and Study Design.

Population (P):

The criteria focus on studies involving livestock, excluding fish unless explicitly directed (fish are separated into a distinct category). The geographic scope is limited to studies conducted in Africa.

Intervention (I):

Eligible studies involve practices like: Pasture and grazing management. Feed experiments (including feed processing). Improved variety and herd density management. Manure management and related practices.

Comparison (C):

Studies should ideally have a control group, except for papers reporting diet and intake where controls are not mandatory.

Outcomes (O):

The review emphasizes studies reporting primary data on key outcomes: Reproductive performance, economics, yields, soil quality, biodiversity, emissions, and labour. Studies that report surveys or specific emission-related outcomes in controlled environments are included, but consultation with the team is recommended.

Study Design (S):

Studies must focus on interventions and primary data collection. Lab experiments or greenhouse studies are excluded unless they focus on emission outcomes.

Data Extraction Process

Data were extracted from tables, text, and figures. Figures were digitized using available software, such as Web Plot Digitizer (https://apps.automeris.io/wpd/).

The data was extracted using a macros enabled excel template:

https://cgiar-my.sharepoint.com/:x:/g/personal/p_steward_cgiar_org/EXUFiZJ-jopBoxHaC9MsBHUBHC-qVT66csvmjvxluJ_CWQ?e=bEG8kY

There are 9 sections to this workbook:
1) Pub = Publication: capture bibliographic data about an experiment here.
2) Site & Site Soils: captures information about experimental sites, any detailed information about soil variables should be captured in Site.Soils
3) ExpD = Experimental design, captures information about the number of study replicates, plot sizes, etc.
4) Practices= These include the following:

System and Housing structures

Time periods

Herd or Group structures

Diets: This is broken into 3 tabs; Indgredient feed to the animals, Nutritional content of each ingredient and the digestibility of those ingredients.

Pasture: In experiments where animals were allowed to graze in the pastures, details of grass species are collected.

Grazing: This includes the amount of time animals were allowed to graze, and whether there were any fertilizer or tillage treatments to the grazing land.

Chemicals, Vaccines, Medicines that were given to the animals during the experiment

  1. Make.Trt = create & name treatments by combining the experimental practices entered in the practice tabs.

  2. EnterData = Captures values and associated errors for the experimental treatments, products, outcomes and times specified in the previous tabs. Do not start data entry until you’ve completed all the previous tabs.

Data Extraction tabs in Excel
Data Extraction tabs in Excel

Data extractors went through an intensive 3 week training that included video tutorials and practical examples of extracting data with the livestock template.

Training materials can be found here: https://cgiar-my.sharepoint.com/:f:/g/personal/p_steward_cgiar_org/EmOgDnJmyX9KkD1s8rNqggsBlSDMvTavst0gd7VIwU3EMw?e=c1gHDf

Livestock ontology

As part of the extraction process of the ERA Livestock, we created an ontology. The livestock ontology is a structured, hierarchical framework that defines and categorizes terms, concepts, and relationships related to livestock systems. It includes standardized definitions for livestock species, breeds, production systems, management practices, feed types, environmental factors, health indicators, and outcomes. By providing a shared vocabulary, livestock ontology facilitates consistent and comprehensive data representation across studies.

Link to the ontology: https://doi.org/10.7910/dvn/75e7hv

Exploring the data

Importing the data

Downloading the data from the S3 bucket

The data can be downloading using the following code:

#download   most    recent  version of  the data    

#   Set up  connection  to  S3  bucket
s3<-s3fs::S3FileSystem$new(anonymous    =   T)
        era_s3<-"s3://digital-atlas/era"
        
        #   List    the files   in  the s3  bucket
        files<-s3$dir_ls(file.path(era_s3,"data"))
        #   This    is  the most    recent  version of  the datas3://digital-atlas/era/data/industrious_elephant_2023-YYYY-MM-DD.RData
        files<-tail(grep(".RData",grep("skinny_cow_2022",files,value=T),value=T),1)
        
        #   Set a   save    location    for the dataset (amend  to  something   more    suitable    for your    needs)
        save_path<-file.path(getwd(),basename(files))
        
        if(!file.exists(save_path)){
          s3$file_download(files,save_path,overwrite    =   T)
        }
#> [1] "C:/Users/JNamita/OneDrive - CGIAR/ERA_dev/R/misc/skinny_cow_2022-2024-12-11.RData"
        
        Tables<-    miceadds::load.Rdata2(file=basename(save_path),path=dirname(save_path))

Subsetting the ERA Compiled data

The Evidence for Resilient Agriculture (ERA) dataset provides a comprehensive analysis of the impacts of technology adoption on 87 key indicators spanning productivity, resilience, and climate change mitigation. This dataset integrates data from both agronomy and livestock studies, with a focus on creating control-versus-treatment comparisons to enable robust meta-analysis.

To extract insights specific to livestock, it is necessary to subset the dataset to isolate livestock-related studies, as follows (Unhide the code):

# Subset data
livestock_data<-ERA.Compiled[!is.na(Date)]
# Subset to fewer themes to simplify plot
livestock_data<-livestock_data[Product.Type %in% c("Animal")]

Exploring geographic locations of studies available

This dataset represents spatial information about livestock-related research papers across various countries in Africa. Each point on the map corresponds to a geographic location associated with a research paper, categorized by livestock type (e.g., cattle, goat, sheep, etc.). The dataset includes:

Country: The country where the research is associated. Latitude and Longitude: Coordinates representing the location of the research. Product.Simple: The type of livestock being studied. Counts: The number of papers associated with each location (aggregated for the map). This data provides an overview of the geographic distribution and focus of livestock research, enabling users to explore patterns and trends by livestock type and region.

# Create a leaflet map with interactive filtering
# Subset the livestock data
site_data <- livestock_data %>%
  dplyr::select(Product.Simple, Country, Latitude, Longitude) %>%
  distinct() %>%
  mutate(
    Longitude = as.numeric(Longitude),
    Latitude = as.numeric(Latitude)
  ) %>%
  filter(!is.na(Longitude) & !is.na(Latitude))  # Remove rows with missing coordinates

# Count the number of records per country
livestock_counts <- site_data %>%
  group_by(Country,Product.Simple) %>%
  summarise(N_Livestock = n())

# Prepare the world map with only African countries
world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  filter(continent == "Africa") %>%  # Filter for African continent only
  mutate(admin = if_else(admin == "United Republic of Tanzania", "Tanzania", admin))

# Ensure the CRS is consistent (EPSG:4326)
world <- st_transform(world, crs = 4326)

# Convert site data to spatial format (only for African countries)
site_data_filtered <- site_data %>%
  filter(Country %in% world$admin) %>%  # Filter site data for African countries
  st_as_sf(coords = c("Longitude", "Latitude"), crs = 4326, remove = FALSE)

# Join livestock counts with African country geometries for mapping
countries_studies <- world %>%
  dplyr::select(admin, geometry) %>%
  rename(Country = admin) %>%
  left_join(livestock_counts, by = "Country")

ui <- fluidPage(
  tags$head(
    tags$style(HTML("
      .main-panel { overflow-y: hidden; }  /* Prevent vertical scrollbars */
      body { overflow-x: hidden; }        /* Prevent horizontal scrollbars */
    "))
  ),
  sidebarLayout(
    sidebarPanel(
      style = "height: auto;",  # Let the sidebar size adjust naturally
      selectInput(
        inputId = "animal_filter",
        label = "Select Livestock Type:",
        choices = c("All", unique(livestock_data$Product.Simple)),
        selected = "All"
      )
    ),
    mainPanel(
      div(
        style = "height: 600px; width: 100%;",  # Set height explicitly for the plot container
        plotOutput("livestockMap", width = "100%", height = "600px")  # Match height
      )
    )
  )
)

# Define Server
server <- function(input, output, session) {
  output$livestockMap <- renderPlot({
    # Filter site data based on user selection
    filtered_data <- if (input$animal_filter == "All") {
      site_data_filtered
    } else {
      site_data_filtered %>%
        filter(Product.Simple == input$animal_filter)
    }
    
    # Generate the ggplot map with points in a single color
    ggplot() +
      geom_sf(data = countries_studies, fill = "gray92", color = "white") +  # Neutral base map
      geom_point(data = filtered_data, aes(x = Longitude, y = Latitude), 
                 size = 3, color = "#3333ff", alpha = 0.8) +  # Single light blue color for points
      annotate(
        "text", x = 40, y = -30, label = "One Point = One Paper", 
        color = "#000000", size = 5, hjust = 0
      ) +  # Add annotation for legend
      theme_minimal() +
      theme(
        axis.line = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position = "none"  # Remove default legend
      ) +
      coord_sf(xlim = c(-20, 95), ylim = c(-40, 40), expand = FALSE)
  })
}

# Run the App
shinyApp(ui = ui, server = server)
Shiny applications not supported in static R Markdown documents

Animals reported within papers

The treemap represents the distribution of livestock categories reported within research papers that were extracted in ERA. Each rectangle corresponds to a livestock category, and its size is proportional to the number of papers reporting on that category. Here’s an analysis of the data shown:

Dominant Categories:

Sheep: The largest rectangle (120 papers) indicates that sheep are the most frequently reported livestock category in the dataset. This could reflect their significant role in agricultural research and practices.

Goats: Goats follow closely with 91 papers, showing their importance, likely due to their adaptability to diverse environments.

Moderately Represented Categories:

Cattle (96 papers) and Fish (71 papers) are also prominent, reflecting their widespread use in agricultural systems, both for food production and other economic activities.

# UI
ui <- fluidPage(
  plotlyOutput("treemapPlot", height = "400px")  # Set plot height
)

# Server
server <- function(input, output, session) {
  output$treemapPlot <- renderPlotly({
    # Prepare data for the treemap
    products <- livestock_data %>%
      distinct(Product.Simple, Code) %>%  # Keep only unique combinations
      group_by(Product.Simple) %>%
      summarise(Row_Count = n(), .groups = "drop")  # Count occurrences per category

    # Create the Plotly treemap
    plot_ly(
      type = "treemap",
      labels = products$Product.Simple,
      parents = NA,  # No hierarchical structure in this case
      values = products$Row_Count,
      textinfo = "label+value",  # Display labels and values
      marker = list(colorscale = "Viridis")  # Apply a color scale
    )
  })
}

# Run the Shiny app
shinyApp(ui, server)
Shiny applications not supported in static R Markdown documents

Outcomes reported

Outcome measures, units, and products were extracted as reported. Outcome codes differentiated plant parts (grain vs stover) and products from the same species (milk versus meat). Oftentimes authors discussed many treatments but only reported a few. The ways in which the outcomes were reported in the papers, by year or aggregation across practices, dictated which data were extracted and how treatments were coded. Experimental Units (EU) described the species or product that were measured (e.g., maize grain).

# Define the UI
# Define the UI
ui <- fluidPage(
  mainPanel(
    plotlyOutput("outcomesPlot", width = "100%", height = "400px")  # Adjust plot dimensions
  )
)

# Define the server logic
server <- function(input, output) {
  output$outcomesPlot <- renderPlotly({
    # Aggregate data by pillar and indicator
    DATA_OUTCOMES <- livestock_data %>%
      group_by(Out.Pillar, Out.Ind) %>%
      summarise(N_Livestock = n_distinct(Code), .groups = "drop") %>%
      rename(Pillar = Out.Pillar, Indicator = Out.Ind)

    # Sort indicators within each pillar for better organization
    DATA_OUTCOMES <- DATA_OUTCOMES %>%
      arrange(Pillar, Indicator) %>%
      mutate(
        Indicator = factor(Indicator, levels = unique(Indicator)),
        Pillar = factor(Pillar, levels = c("Productivity", "Resilience", "Mitigation"))
      )

    # Define hover information for specific indicators
    hover_info <- tibble(
      Indicator = c("Product Yield", "Income", "Economic Performance","Costs", "Soil Quality","Efficiency"),
      HoverDetails = c("Meat yield; Egg yield; Milk yield; Reproductive yield; Weight gain","Gross return; Gross margin; Net return","Benefit Cost Ratios","Labour cost; Total cost; Fixed cost; Variable cost","Soil Erosion; Soil Nitrogen; Soil Organic Carbon","Feed Conversion Ratios; Protein Conversion Ratios; Nitrogen Apparent Efficiency")
    )

    # Add hover text to the data
    DATA_OUTCOMES <- DATA_OUTCOMES %>%
      left_join(hover_info, by = "Indicator") %>%  # Join hover info
      mutate(
        HoverText = ifelse(
          !is.na(HoverDetails),
          paste0("<b>", Indicator, "</b><br>", HoverDetails, "<br><i>Count:</i> ", N_Livestock),
          paste0("<b>", Indicator, "</b><br><i>Count:</i> ", N_Livestock)
        )
      )

    # Define custom colors for each pillar
    pillar_colors <- c(
      "Productivity" = "#FFCC66",
      "Resilience" = "#990000",
      "Mitigation" = "#8B4513"
    )

    # Create the faceted bar plot with ggplot
    p <- ggplot(DATA_OUTCOMES, aes(
      x = Indicator, y = N_Livestock, fill = Pillar, text = HoverText
    )) +
      geom_col(width = 0.8) +  # Vertical bars
      labs(
        y = "Number of Papers",
        x = "Outcome Indicator"
      ) +
      scale_fill_manual(values = pillar_colors, na.value = "gray70") +
      scale_y_continuous(limits = c(0, max(DATA_OUTCOMES$N_Livestock) + 50), expand = c(0, 0)) +
      facet_wrap(~Pillar, scales = "free_x", nrow = 1) +
      theme_minimal() +
      theme(
        axis.text.x = element_text(size = 10, angle = 45, hjust = 1),  # Rotate x-axis labels
        axis.text.y = element_text(size = 10),  # Adjust y-axis labels
        axis.title.x = element_text(size = 12, face = "bold", margin = margin(t = 10)),  # Add margin
        axis.title.y = element_text(size = 12, face = "bold"),  # Y-axis title styling
        legend.position = "none",  # Remove legend
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        strip.text = element_text(size = 12, face = "bold"),  # Facet strip labels styling
        plot.margin = margin(10, 10, 10, 10)  # Reduced margins
      )

    # Convert ggplot to a plotly object
    ggplotly(p, tooltip = "text")  # Use hover text for tooltips
  })
}

# Run the app
shinyApp(ui = ui, server = server)
Shiny applications not supported in static R Markdown documents

Digestibility and nutrition

To explore the diet items and associated nutrition and digestibility of these items, we need to use the metadata of ERA, and not ERA.Compiled. Unhide the code below and adjust the script with variables you may be interested in.

  # First we need to get the detailed dataset containing the management information that relates to the experiment, this is in the Atlas S3 Bucket
  s3<-s3fs::S3FileSystem$new(anonymous = T)
  era_s3<-"s3://digital-atlas/era"
  
  # List the files in the s3 bucket
  files<-s3$dir_ls(file.path(era_s3,"data"))
  # This is the most recent version of the datas3://digital-atlas/era/data/skinny_cow_2022-YYYY-MM-DD.RData (substitute most recent date into filepath)
  files<-tail(grep(".RData",grep("skinny_cow_2022",files,value=T),value=T),1)
  
  # Set a save location for the dataset (amend to something more suitable for your needs)
  save_path<-file.path(getwd(),basename(files))
  
  if(!file.exists(save_path)){
    s3$file_download(files,save_path,overwrite = T)
  }
  
  livestock_metadata<- miceadds::load.Rdata2(file=basename(save_path),path=dirname(save_path))
  names(livestock_metadata)
#>  [1] "Pub.Out"             "Site.Out"            "Soil.Out"           
#>  [4] "ExpD.Out"            "Times.Out"           "Prod.Out"           
#>  [7] "Var.Out"             "Chems.Code"          "Chems.Out"          
#> [10] "AF.Out"              "Animals.Out"         "Animals.Diet"       
#> [13] "Animals.Diet.Comp"   "Animals.Diet.Digest" "Other.Out"          
#> [16] "MT.Out"              "Out.Out"             "Data.Out"
  
  # 4.2) Explore  metadata #####
  # This table is a high level description of the diet
  diet_summary<-livestock_metadata$Animals.Out
  
    diet_ingredients<-livestock_metadata$Animals.Diet
  head(diet_ingredients)
#>    D.Item.AOM  D.ItemxProcess_low A.Level.Name               D.Type
#>        <char>              <char>       <char>               <char>
#> 1: AOM_001911                 hay         Base   Herbaceous Fodders
#> 2: AOM_000604    groundnut haulms     150g/day       Crop Byproduct
#> 3: AOM_000604    groundnut haulms     300g/day       Crop Byproduct
#> 4: AOM_000604    groundnut haulms     450g/day       Crop Byproduct
#> 5: AOM_001901 atriplex nummularia         Base Agroforestry Fodders
#> 6: AOM_000647        barley grain           T2         Crop Product
#>                   D.Item D.Item.Group D.Source D.Process DC.Is.Dry D.Amount
#>                   <char>       <char>   <char>    <list>    <char>    <num>
#> 1: Unspecified Grass Hay         <NA>     <NA>        NA       Yes       NA
#> 2:       Groundnut Haulm         <NA>     <NA>        NA       Yes      150
#> 3:       Groundnut Haulm         <NA>     <NA>        NA       Yes      300
#> 4:       Groundnut Haulm         <NA>     <NA>        NA       Yes      450
#> 5:   Atriplex nummularia         <NA>     <NA>        NA      <NA>       NA
#> 6:          Barley Grain         <NA>     <NA>        NA      <NA>      400
#>    D.Ad.lib D.Unit.Amount D.Unit.Time D.Unit.Animals D.Day.Start D.Day.End
#>      <char>        <char>      <char>         <char>      <lgcl>    <char>
#> 1:      Yes          g/kg         day     individual          NA      <NA>
#> 2:       No             g         day     individual          NA      <NA>
#> 3:       No             g         day     individual          NA      <NA>
#> 4:       No             g         day     individual          NA      <NA>
#> 5:      Yes          <NA>         day           <NA>          NA      <NA>
#> 6:     <NA>             g         day           <NA>          NA      <NA>
#>    B.Code          D.Item.Raw      D.ItemxProcess D.Is.Group
#>    <char>              <char>              <char>     <lgcl>
#> 1: NN0022                 Hay                 Hay      FALSE
#> 2: NN0022    Groundnut Haulms    Groundnut Haulms      FALSE
#> 3: NN0022    Groundnut Haulms    Groundnut Haulms      FALSE
#> 4: NN0022    Groundnut Haulms    Groundnut Haulms      FALSE
#> 5: NN0080 Atriplex nummularia Atriplex nummularia      FALSE
#> 6: NN0080        Barley Grain        Barley Grain      FALSE
#>       D.Item.Root.Comp D.Item.Root.Comp.Proc_Major
#>                 <char>                      <char>
#> 1:   Unspecified Grass     Unspecified Grass Dried
#> 2:     Groundnut Haulm             Groundnut Haulm
#> 3:     Groundnut Haulm             Groundnut Haulm
#> 4:     Groundnut Haulm             Groundnut Haulm
#> 5: Atriplex nummularia         Atriplex nummularia
#> 6:        Barley Grain                Barley Grain
#>    D.Item.Root.Other.Comp.Proc_All D.Item.Other D.Item.Proc_All
#>                             <char>       <char>          <char>
#> 1:           Unspecified Grass Hay         <NA>             Hay
#> 2:                 Groundnut Haulm         <NA>            <NA>
#> 3:                 Groundnut Haulm         <NA>            <NA>
#> 4:                 Groundnut Haulm         <NA>            <NA>
#> 5:             Atriplex nummularia         <NA>            <NA>
#> 6:                    Barley Grain         <NA>            <NA>
#>    D.Item.Proc_Major D.Item.Proc_Minor D.Item.Comp
#>               <char>            <char>      <char>
#> 1:             Dried              <NA>        <NA>
#> 2:              <NA>              <NA>       Haulm
#> 3:              <NA>              <NA>       Haulm
#> 4:              <NA>              <NA>       Haulm
#> 5:              <NA>              <NA>        <NA>
#> 6:              <NA>              <NA>       Grain
#>                                                                           AOM.Terms
#>                                                                              <char>
#> 1: Forage Plants/Cereal and Grass Forages/Unspecified Grass/Unspecified Grass Dried
#> 2:                       Crop Byproduct/Legume ByProducts/Groundnut/Groundnut Haulm
#> 3:                       Crop Byproduct/Legume ByProducts/Groundnut/Groundnut Haulm
#> 4:                       Crop Byproduct/Legume ByProducts/Groundnut/Groundnut Haulm
#> 5:                                   Forage Plants/Forage Trees/Atriplex nummularia
#> 6:                                 Crop Product/Cereal Products/Barley/Barley Grain
#>    AOM.Scientific.Name D.Item.Is.Tree          D.Item_raw
#>                 <char>         <lgcl>              <char>
#> 1:                <NA>          FALSE                 Hay
#> 2:    Arachis hypogaea          FALSE    Groundnut Haulms
#> 3:    Arachis hypogaea          FALSE    Groundnut Haulms
#> 4:    Arachis hypogaea          FALSE    Groundnut Haulms
#> 5: Atriplex nummularia           TRUE Atriplex nummularia
#> 6:     Hordeum vulgare          FALSE        Barley Grain
  
  diet_nutrition<-livestock_metadata$Animals.Diet.Comp
  head(diet_nutrition)
#>                D.Item   B.Code DC.Unit.Is.Dry is_group is_entire_diet
#>                <char>   <char>         <char>   <lgcl>         <lgcl>
#> 1:         0 Finisher NN0137.2    Unspecified     TRUE          FALSE
#> 2:           0 Grower NN0137.2    Unspecified     TRUE          FALSE
#> 3:                0 M   NN0005            Yes    FALSE           TRUE
#> 4:              0 MSK HK0066.1    Unspecified    FALSE           TRUE
#> 5:              0 MSK HK0066.2    Unspecified    FALSE           TRUE
#> 6: 0 g/kg A. tortilis   HK0330            Yes    FALSE           TRUE
#>    D.Item.Root.Other.Comp.Proc_All DC.Value DC.Unit DC.Method DC.Variable
#>                             <char>    <num>  <char>    <char>      <char>
#> 1:                            <NA>       NA    <NA>      <NA>          DM
#> 2:                            <NA>       NA    <NA>      <NA>          DM
#> 3:                            <NA>      876    g/kg  Measured          DM
#> 4:                            <NA>      921    g/kg  Measured          DM
#> 5:                            <NA>      881    g/kg  Measured          DM
#> 6:                            <NA>      867    g/kg  Measured          DM
#>            D.Item_raw
#>                <char>
#> 1:         0 Finisher
#> 2:           0 Grower
#> 3:                0 M
#> 4:              0 MSK
#> 5:              0 MSK
#> 6: 0 g/kg A. tortilis
  
  diet_digestibility<-livestock_metadata$Animals.Diet.Digest
  head(diet_digestibility)
#>               D.Item B.Code DD.Unit.Is.Dry is_group is_entire_diet
#>               <char> <char>         <char>   <lgcl>         <lgcl>
#> 1:             0 PPL AN0057            Yes    FALSE           TRUE
#> 2:  0% A. sieberiana LM0104            Yes    FALSE           TRUE
#> 3: 0% Brewer's Grain HK0033    Unspecified    FALSE           TRUE
#> 4:            0% CFL EO0046            Yes    FALSE           TRUE
#> 5:  0% Leucaena Diet HK0108    Unspecified    FALSE           TRUE
#> 6:  0% Leucaena Goat HK0008    Unspecified    FALSE           TRUE
#>    D.Item.Root.Other.Comp.Proc_All DD.Value     DD.Unit DD.Nut.or.Diet
#>                             <char>    <num>      <char>         <char>
#> 1:                            <NA>     0.57 Coefficient       Nutrient
#> 2:                            <NA>    57.80           %       Nutrient
#> 3:                            <NA>    69.86           %       Nutrient
#> 4:                            <NA>   530.90        g/kg       Nutrient
#> 5:                            <NA>    71.75           %       Nutrient
#> 6:                            <NA>    50.80           %       Nutrient
#>                                         DD.Method DD.Variable        D.Item_raw
#>                                            <char>      <char>            <char>
#> 1:            Measured total collection techinque          DM             0 PPL
#> 2:                                    Unspecified          DM  0% A. sieberiana
#> 3: Measured total collection techinque (Apparent)          DM 0% Brewer's Grain
#> 4:            Measured total collection techinque          DM            0% CFL
#> 5: Measured total collection techinque (Apparent)          DM  0% Leucaena Diet
#> 6:                                    Unspecified          DM  0% Leucaena Goat

Diet ingredients table

  # Display the summary table   of  diets
DT::datatable(
        diet_ingredients,
        options =   list(
                scrollY =   "400px",        #   Set vertical    scroll  height
                pageLength  =   20                  #   Initial number  of  rows    displayed
        )
)

Diet nutrition table


DT::datatable(
  diet_nutrition,
  options = list(
    scrollY = "400px", 
    pageLength = 10,  # Show only 10 rows per page
    lengthMenu = c(10, 20, 50, 100)  # Allow user to choose the number of rows displayed
  )
)

Here is a summary of the diet digestibility table

  # Display the summary table   of  diets
DT::datatable(
        diet_digestibility,
        options =   list(
                scrollY =   "400px",        #   Set vertical    scroll  height
                pageLength  =   20                  #   Initial number  of  rows    displayed
        )
)